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LanceDB is a multimodal lakehouse for AI, built on top of Lance, an open-source lakehouse format. Below, we list a few ways LanceDB can help you build and scale your AI and ML workloads.
1

High-performance random access and data management for model training

Use LanceDB to curate, explore and distribute very large multimodal datasets for training and fine-tuning models. LanceDB comes with built-in table versioning, schema evolution, and fast random access, making it far more efficient to do dataset slicing, sampling, filtering and shuffles on large, rapidly evolving datasets.
2

Massively scalable, fast and high-quality retrieval − without breaking the bank

Use LanceDB as the data + retrieval layer for production AI workloads: RAG, agents, semantic search, recommendation systems, and more. Keep multimodal data, metadata, and embeddings in the same table and query them via vector search, full-text search or SQL. Easily add new features (columns in your tables) as your application evolves, without copying existing data.
LanceDB is designed for a variety of workloads and deployment scenarios, and supports use cases that are way beyond traditional vector search. The LanceDB suite includes LanceDB OSS, an open-source embedded library, and LanceDB Enterprise, a distributed and managed multimodal lakehouse. Both are built on top of the same open-source Lance format and table abstractions.

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Use cases

  • Search: Build high-performance search and retrieval applications using LanceDB’s optimized storage, including vector search, full-text search, and hybrid search with secondary indexes.
  • Data Curation: Manage and filter on petabyte-scale multimodal datasets, including video and point cloud data, to gain insights, explore data and inform model development.
  • Feature engineering: Add new columns (features), create embeddings, and transform your data at scale. LanceDB lets you extend tables both vertically and horizontally with minimal I/O overhead.
  • Training: Efficiently access and manage large-scale multimodal datasets for training and fine-tuning AI models.

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Choose how you run LanceDB

Depending on your needs, you can choose one of the following ways to run LanceDB.

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1. LanceDB OSS

The fastest way to get started is the open-source embedded library, with client SDKs in Python, TypeScript and Rust. Run it locally during development, then use the same data model and APIs as you scale up and need a managed solution. Start here:

Quickstart

Get started with LanceDB in minutes.

Basic Table Operations

Create tables, search vectors, and modify data in LanceDB.

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2. LanceDB Enterprise

LanceDB Enterprise is a distributed and managed multimodal lakehouse built for search, curation, feature engineering, and training-oriented data access workflows on top of the same core table abstraction. This eliminates the need for teams to build bespoke infrastructure to manage petabyte-scale multimodal datasets. To set up LanceDB Enterprise in your organization, reach out to us at contact@lancedb.com.
Built with scale, performance, and security in mind.LanceDB Enterprise is designed for very large-scale, high-performance, distributed workloads in private deployments, and can operate under strict security requirements.

Quickstart

Get started with LanceDB Enterprise in minutes.